





'''simple docstring'''






import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification






def   __A    (				a_    :	Any						):
		lowerCAmelCase  :     Any       						= []
		embed.append(
		    (
		        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
		        f'''stage{idx}.patch_embed.proj.weight''',
		    )						)
		embed.append(
		    (
		        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
		        f'''stage{idx}.patch_embed.proj.bias''',
		    )						)
		embed.append(
		    (
		        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
		        f'''stage{idx}.patch_embed.norm.weight''',
		    )						)
		embed.append(
		    (
		        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
		        f'''stage{idx}.patch_embed.norm.bias''',
		    )						)
		return embed






def   __A    (				a_    :	Tuple   ,a_    :	Any						):
		lowerCAmelCase  :     Dict       						= []
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
		        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
		        f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
		        f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
		        f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
		        f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
		    )						)
		attention_weights.append(
		    (
		        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
		        f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
		    )						)
		attention_weights.append(
		    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')						)
		attention_weights.append(
		    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')						)
		attention_weights.append(
		    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')						)
		attention_weights.append(
		    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')						)
		attention_weights.append(
		    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''')						)
		attention_weights.append(
		    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''')						)
		attention_weights.append(
		    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''')						)
		attention_weights.append(
		    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''')						)
		return attention_weights






def   __A    (				a_    :	str						):
		lowerCAmelCase  :     Optional[int]       						= []
		token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token")						)
		return token






def   __A    (				):
		lowerCAmelCase  :     Union[str, Any]       						= []
		head.append(("layernorm.weight", "norm.weight")						)
		head.append(("layernorm.bias", "norm.bias")						)
		head.append(("classifier.weight", "head.weight")						)
		head.append(("classifier.bias", "head.bias")						)
		return head






def   __A    (				a_    :	List[str]   ,a_    :	Tuple   ,a_    :	List[Any]   ,a_    :	Optional[Any]						):
		lowerCAmelCase  :     str       						= "imagenet-1k-id2label.json"
		lowerCAmelCase  :     List[str]       						= 1_0_0_0

		lowerCAmelCase  :     Tuple       						= "huggingface/label-files"
		lowerCAmelCase  :     Optional[Any]       						= num_labels
		lowerCAmelCase  :     Optional[int]       						= json.load(open(cached_download(hf_hub_url(a_   ,a_   ,repo_type="dataset"						)						)   ,"r"						)						)
		lowerCAmelCase  :     List[str]       						= {int(a_						): v for k, v in idalabel.items()}

		lowerCAmelCase  :     Dict       						= idalabel
		lowerCAmelCase  :     Optional[int]       						= {v: k for k, v in idalabel.items()}

		lowerCAmelCase  :     Optional[Any]       						= CvtConfig(num_labels=a_   ,idalabel=a_   ,labelaid=a_						)

		# For depth size 13 (13 = 1+2+10)
		if cvt_model.rsplit("/"   ,1						)[-1][4:6] == "13":
				lowerCAmelCase  :     Tuple       						= [1, 2, 1_0]

		# For depth size 21 (21 = 1+4+16)
		elif cvt_model.rsplit("/"   ,1						)[-1][4:6] == "21":
				lowerCAmelCase  :     Tuple       						= [1, 4, 1_6]

		# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
		else:
				lowerCAmelCase  :     Optional[int]       						= [2, 2, 2_0]
				lowerCAmelCase  :     Any       						= [3, 1_2, 1_6]
				lowerCAmelCase  :     Tuple       						= [1_9_2, 7_6_8, 1_0_2_4]

		lowerCAmelCase  :     List[Any]       						= CvtForImageClassification(a_						)
		lowerCAmelCase  :     List[Any]       						= AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"						)
		lowerCAmelCase  :     Optional[Any]       						= image_size
		lowerCAmelCase  :     str       						= torch.load(a_   ,map_location=torch.device("cpu"						)						)

		lowerCAmelCase  :     List[str]       						= OrderedDict()
		lowerCAmelCase  :     int       						= []

		for idx in range(len(config.depth						)						):
				if config.cls_token[idx]:
						lowerCAmelCase  :     str       						= list_of_state_dict + cls_token(a_						)
				lowerCAmelCase  :     Optional[Any]       						= list_of_state_dict + embeddings(a_						)
				for cnt in range(config.depth[idx]						):
						lowerCAmelCase  :     Tuple       						= list_of_state_dict + attention(a_   ,a_						)

		lowerCAmelCase  :     Union[str, Any]       						= list_of_state_dict + final()
		for gg in list_of_state_dict:
				print(a_						)
		for i in range(len(a_						)						):
				lowerCAmelCase  :     Tuple       						= original_weights[list_of_state_dict[i][1]]

		model.load_state_dict(a_						)
		model.save_pretrained(a_						)
		image_processor.save_pretrained(a_						)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					parser.add_argument(
					    """--cvt_model""",
					    default="""cvt-w24""",
					    type=str,
					    help="""Name of the cvt model you'd like to convert.""",
					)
					parser.add_argument(
					    """--image_size""",
					    default=3_84,
					    type=int,
					    help="""Input Image Size""",
					)
					parser.add_argument(
					    """--cvt_file_name""",
					    default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
					    type=str,
					    help="""Input Image Size""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)

					lowerCAmelCase									=   parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)










'''simple docstring'''






import random
from typing import Any






def   __A    (				a_    :	list						):
		for _ in range(len(a_						)						):
				lowerCAmelCase  :     List[Any]       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase  :     Tuple       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= data[b], data[a]
		return data


if __name__ == "__main__":
					lowerCAmelCase									=   [0, 1, 2, 3, 4, 5, 6, 7]
					lowerCAmelCase									=   ["""python""", """says""", """hello""", """!"""]
					print("""Fisher-Yates Shuffle:""")
					print("""List""", integers, strings)
					print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



